Alex Petrov's Research Projects

My research combines behavioral experimentation with computational and
mathematical modeling of cognition, paying close attention to neurological
constraints. I am particularly interested in learning, adaptation, and the
dynamic aspects of perception, memory, categorization, reasoning, and
executive control, as well as their neural implementation.
I often use state-of-the-art psychophysical methods to track the dynamics
of cognition at time scales from tens of milliseconds to several days.
I favor decentralized and interactive models -- connectionist, symbolic,
or mathematical -- that incorporate biologically plausible learning
mechanisms and are informed by an integrated cognitive architecture.
Coherent global behavior emerges from local interactions in a
steady state that changes adaptively whenever the environment changes.

We live in very exciting times -- for the first time in human history a
consistent and empirically grounded picture of how the brain creates the
mind begins to emerge. Cognitive science today seems at the threshold of
a breakthrough comparable to that of genetics in the 1950s.
This is an adventure of a lifetime and I am very enthusiastic to
participate.

This interdiciplinary project combines experimentation and modeling
to study the neural mechanisms of perceptual learning. A series of
psychophysical studies probe the dynamics of perceptual learning in
non-stationary environments, both with and without feedback.
Practicing an orientation-discrimination task gradually improves
performance but there are significant switch costs
(interference) whenever the context surrounding the target stimuli
changes. In a recent Psychological Review article
(Petrov, Dosher, & Lu, 2005)
we provide an existence proof that incremental Hebbian reweighting
can account quantitatively for the complex pattern of learning and
switch-costs in our non-stationary training protocol. The model takes
grayscale images as inputs, produces binary responses as outputs, and
improves its discrimination accuracy incrementally with practice with
no need for external feedback. The model performance is thus directly
comparable to the human data. Learning occurs only in the read-out
connections to a decision unit in a neural network; the stimulus
representations never change.

This interdiciplinary project combines experimentation and modeling
at the intersection of psychophysics and memory. A series of studies
revealed that human response distributions are markedly non-stationary
and non-uniform even when the stimulus distributions are stationary and
uniform. Moreover, skewed stimulus distributions induce context effects
in opposite directions depending on the presence or absence of feedback.
In a recent Psychological Review article
(Petrov & Anderson, 2005)
we demonstrated that a memory-based model accounts naturally and
quantitatively for these and many other dynamic effects in category
rating and absolute identification. The
ANCHOR model
maintains a set of adjustable anchors that compete to match
the perceived magnitude of the target stimulus. An explicit correction
strategy determines the final response. Two incremental learning
mechanisms track the statistics of the stimulus distribution and make
the system very adaptive but also prone to sequential, context, transfer,
and priming effects. The rating scale unfolds as an adaptive map from
a single arbitrarily placed anchor with no need for external feedback.

My Ph.D. dissertation involved a model of analogy making called
AMBR.
It explores the fundamental cognitive ability of interpreting a novel
situation in terms of a similar situation that is already familiar.
The model is based on the hybrid symbolic/connectionist
architecture DUAL
and employs its dynamic emergent style of computation to account for
the flexible and context-sensitive nature of human analogy making.
The decentralized representations of episodes support gradual and
reconstructive memory retrieval that is inextricably intertwined with
the process of analogical mapping (Kokinov & Petrov, 2001).